class_docstr: AutoGAT. The model used in this automodel is GAT, i.e., the graph attentional network from the `"Graph Attention Networks"
  <https://arxiv.org/abs/1710.10903>`_ paper. The layer is
  
  .. math::
      \mathbf{x}^{\prime}_i = \alpha_{i,i}\mathbf{\Theta}\mathbf{x}_{i} +
      \sum_{j \in \mathcal{N}(i)} \alpha_{i,j}\mathbf{\Theta}\mathbf{x}_{j}
  
  where the attention coefficients :math:`\alpha_{i,j}` are computed as
  
  .. math::
      \alpha_{i,j} =
      \frac{
      \exp\left(\mathrm{LeakyReLU}\left(\mathbf{a}^{\top}
      [\mathbf{\Theta}\mathbf{x}_i \, \Vert \, \mathbf{\Theta}\mathbf{x}_j]
      \right)\right)}
      {\sum_{k \in \mathcal{N}(i) \cup \{ i \}}
      \exp\left(\mathrm{LeakyReLU}\left(\mathbf{a}^{\top}
      [\mathbf{\Theta}\mathbf{x}_i \, \Vert \, \mathbf{\Theta}\mathbf{x}_k]
      \right)\right)}.
  
  Parameters
  ----------
  num_features: `int`.
      The dimension of features.
  
  num_classes: `int`.
      The number of classes.
  
  device: `torch.device` or `str`
      The device where model will be running on.
  
  init: `bool`.
      If True(False), the model will (not) be initialized.
  
  args: Other parameters.
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code: def _initialize(self):
      # """Initialize model."""
      self._model = GAT(
          {
              "features_num": self.input_dimension,
              "num_class": self.output_dimension,
              **self.hyper_parameters,
          }
      ).to(self.device)
  
file_path: AutoGL/autogl/module/model/pyg/gat.py
imports: import torch
  import torch.nn.functional
  from torch_geometric.nn import GATConv
  from .None import register_model
  from .base import BaseAutoModel, activate_func
  from ....utils import get_logger
label_desc: Use image node_id 2 for calling the AutoGAT obj's underlying member method code with example usage: obj._initialize() without return types